Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python

Code for NeurIPS 2025 paper - Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

Implementation of the Paper "Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models".

Implementation of linear CorEx and temporal CorEx.

Different optimization algorithms like Hill climbing, Simulated annealing, Late accepted Hill climbing , Genetic Algorithm is implemented from scratch.

Code accompanying the paper "Globally Optimal Learning for Structured Elliptical Losses", published at NeurIPS 2019

A Python front-end for the large-scale graphical LASSO optimizer BigQUIC (written in R).